{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logical and\n",
      "epoch 1 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 2  [w[0]=1 w[1]=2 b=0 d=0 y=1 delta_w[0]=1 delta_w[1]=0 delta_b=1]\n",
      "epoch 1 sample 3  [w[0]=0 w[1]=2 b=-1 d=0 y=1 delta_w[0]=0 delta_w[1]=1 delta_b=1]\n",
      "epoch 1 sample 4  [w[0]=0 w[1]=1 b=-2 d=1 y=0 delta_w[0]=-1 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 2 sample 1  [w[0]=1 w[1]=2 b=-1 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 2  [w[0]=1 w[1]=2 b=-1 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 3  [w[0]=1 w[1]=2 b=-1 d=0 y=1 delta_w[0]=0 delta_w[1]=1 delta_b=1]\n",
      "epoch 2 sample 4  [w[0]=1 w[1]=1 b=-2 d=1 y=0 delta_w[0]=-1 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 3 sample 1  [w[0]=2 w[1]=2 b=-1 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 2  [w[0]=2 w[1]=2 b=-1 d=0 y=1 delta_w[0]=1 delta_w[1]=0 delta_b=1]\n",
      "epoch 3 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 1  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 2  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 3  [w[0]=1 w[1]=2 b=-2 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 4  [w[0]=1 w[1]=2 b=-2 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "logical or\n",
      "epoch 1 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 6 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 7 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 8 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 9 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 10 sample 4  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "logical xor\n",
      "epoch 1 sample 1  [w[0]=1 w[1]=2 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 2  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 3  [w[0]=1 w[1]=2 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 1 sample 4  [w[0]=1 w[1]=2 b=0 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 2 sample 1  [w[0]=0 w[1]=1 b=-1 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 2  [w[0]=0 w[1]=1 b=-1 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 2 sample 3  [w[0]=1 w[1]=1 b=0 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 2 sample 4  [w[0]=1 w[1]=1 b=0 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 3 sample 1  [w[0]=0 w[1]=0 b=-1 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 3 sample 2  [w[0]=0 w[1]=0 b=-1 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 3 sample 3  [w[0]=1 w[1]=0 b=0 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 3 sample 4  [w[0]=1 w[1]=1 b=1 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 4 sample 1  [w[0]=0 w[1]=0 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 2  [w[0]=0 w[1]=0 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 4 sample 3  [w[0]=1 w[1]=0 b=1 d=1 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 4 sample 4  [w[0]=1 w[1]=0 b=1 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 5 sample 1  [w[0]=0 w[1]=-1 b=0 d=0 y=0 delta_w[0]=0 delta_w[1]=0 delta_b=0]\n",
      "epoch 5 sample 2  [w[0]=0 w[1]=-1 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 5 sample 3  [w[0]=1 w[1]=-1 b=1 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 5 sample 4  [w[0]=1 w[1]=0 b=2 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 6 sample 1  [w[0]=0 w[1]=-1 b=1 d=0 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=1]\n",
      "epoch 6 sample 2  [w[0]=0 w[1]=-1 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 6 sample 3  [w[0]=1 w[1]=-1 b=1 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 6 sample 4  [w[0]=1 w[1]=0 b=2 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 7 sample 1  [w[0]=0 w[1]=-1 b=1 d=0 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=1]\n",
      "epoch 7 sample 2  [w[0]=0 w[1]=-1 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 7 sample 3  [w[0]=1 w[1]=-1 b=1 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 7 sample 4  [w[0]=1 w[1]=0 b=2 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 8 sample 1  [w[0]=0 w[1]=-1 b=1 d=0 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=1]\n",
      "epoch 8 sample 2  [w[0]=0 w[1]=-1 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 8 sample 3  [w[0]=1 w[1]=-1 b=1 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 8 sample 4  [w[0]=1 w[1]=0 b=2 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 9 sample 1  [w[0]=0 w[1]=-1 b=1 d=0 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=1]\n",
      "epoch 9 sample 2  [w[0]=0 w[1]=-1 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 9 sample 3  [w[0]=1 w[1]=-1 b=1 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 9 sample 4  [w[0]=1 w[1]=0 b=2 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n",
      "epoch 10 sample 1  [w[0]=0 w[1]=-1 b=1 d=0 y=1 delta_w[0]=0 delta_w[1]=0 delta_b=1]\n",
      "epoch 10 sample 2  [w[0]=0 w[1]=-1 b=0 d=1 y=0 delta_w[0]=-1 delta_w[1]=0 delta_b=-1]\n",
      "epoch 10 sample 3  [w[0]=1 w[1]=-1 b=1 d=1 y=0 delta_w[0]=0 delta_w[1]=-1 delta_b=-1]\n",
      "epoch 10 sample 4  [w[0]=1 w[1]=0 b=2 d=0 y=1 delta_w[0]=1 delta_w[1]=1 delta_b=1]\n"
     ]
    }
   ],
   "source": [
    "#!/usr/bin/env python3\n",
    "import numpy as np\n",
    "\n",
    "samples_and = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 0],\n",
    "    [0, 1, 0],\n",
    "    [1, 1, 1],\n",
    "]\n",
    "\n",
    "\n",
    "samples_or = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 1],\n",
    "    [0, 1, 1],\n",
    "    [1, 1, 1],\n",
    "]\n",
    "\n",
    "\n",
    "samples_xor = [\n",
    "    [0, 0, 0],\n",
    "    [1, 0, 1],\n",
    "    [0, 1, 1],\n",
    "    [1, 1, 0],\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "def perceptron(samples):\n",
    "    \n",
    "    '''\n",
    "    w: 初始化shape为2*1的权重系数的向量\n",
    "    b: 初始化bias\n",
    "    a: 固定学习率\n",
    "    '''\n",
    "    w = np.array([1, 2])\n",
    "    b = 0\n",
    "    a = 1\n",
    "\n",
    "    for i in range(10):\n",
    "        for j in range(4):\n",
    "            '''\n",
    "            x: 取参数每行的前两位作为输入\n",
    "            y: 经过阶跃激活函数激活后的输出，y=w*x+b\n",
    "            d: 取参数每行最后一位数字作为真实值\n",
    "            '''\n",
    "            x = np.array(samples[j][:2])\n",
    "            y = 1 if np.dot(w, x) + b > 0 else 0\n",
    "            d = np.array(samples[j][2])\n",
    "            '''\n",
    "            d-y = d-w*x-b，则损失函数为loss=1/2(d-y)^2\n",
    "            delta_b: 对损失函数的b求偏导\n",
    "            delta_w: 对损失函数的w向量求偏导\n",
    "            '''\n",
    "            delta_b = a*(d-y)*(-1)\n",
    "            delta_w = a*(d-y)*(-x)\n",
    "\n",
    "            print('epoch {} sample {}  [w[0]={} w[1]={} b={} d={} y={} delta_w[0]={} delta_w[1]={} delta_b={}]'.format(\n",
    "                i+1, j+1, w[0], w[1], b, d, y, delta_w[0], delta_w[1], delta_b\n",
    "            ))\n",
    "            '''\n",
    "            对系数w和偏置b进行梯度下降运算\n",
    "            '''\n",
    "            w = w - delta_w\n",
    "            b = b - delta_b\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    print('logical and')\n",
    "    perceptron(samples_and)\n",
    "    print('logical or')\n",
    "    perceptron(samples_or)\n",
    "    print('logical xor')\n",
    "    perceptron(samples_xor)\n"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "传统感知器的作用是在一个（超）平面中，通过一条直线进行划分，即可根据直线的两边进行输出值的分类，所以单个感知器可进行简单的逻辑判断（与或非）  \n",
    "异或(xor)的问题在于单凭一条直线无法对当前的平面进行正确划分，所以至少需要两个感知器组成单隐层结构，先对输入进行两次运算，形成两条直线，进而划分出一个区域（中间地带），才能完成正确的输出\n",
    "![image.png](attachment:image.png)"
   ]
  }
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